{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "# utils/core/algs/noisy_benchmarking.py\n",
    "from qiskit_aer.noise import NoiseModel\n",
    "from qiskit_aer import AerSimulator\n",
    "from qiskit_aer.noise.errors import thermal_relaxation_error, depolarizing_error, ReadoutError\n",
    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    "from qiskit_aer.primitives import SamplerV2 as Sampler\n",
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import List, Dict, Tuple, Optional, Union\n",
    "import random\n",
    "\n",
    "\n",
    "class NoisyBenchmark:\n",
    "    \"\"\"\n",
    "    A class for simulating random benchmarking with various noise models.\n",
    "    \n",
    "    This class allows simulation of quantum circuits with customizable noise parameters\n",
    "    including T1/T2 relaxation, depolarizing noise, and readout errors.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, \n",
    "                num_qubits: int = 2,\n",
    "                t1_times: Union[List[float], float] = 50e-6,  # in seconds\n",
    "                t2_times: Union[List[float], float] = 70e-6,  # in seconds\n",
    "                depolarizing_probs: Union[List[float], float] = 0.001,\n",
    "                readout_errors: Union[List[Tuple[float, float]], Tuple[float, float]] = (0.01, 0.01),\n",
    "                gate_times: Dict[str, float] = None):\n",
    "        \"\"\"\n",
    "        Initialize the noisy benchmark simulation.\n",
    "        \n",
    "        Args:\n",
    "            num_qubits: Number of qubits to use in the simulation\n",
    "            t1_times: T1 relaxation times in seconds (per qubit or single value for all qubits)\n",
    "            t2_times: T2 dephasing times in seconds (per qubit or single value for all qubits)\n",
    "            depolarizing_probs: Depolarizing error probabilities (per qubit or single value)\n",
    "            readout_errors: Readout error probabilities as (p_0|1, p_1|0) tuples\n",
    "                            (per qubit or single value for all qubits)\n",
    "            gate_times: Dictionary mapping gate names to their execution times in seconds\n",
    "        \"\"\"\n",
    "        self.num_qubits = num_qubits\n",
    "        \n",
    "        # Convert single values to lists for all qubits\n",
    "        if isinstance(t1_times, (int, float)):\n",
    "            self.t1_times = [t1_times] * num_qubits\n",
    "        else:\n",
    "            self.t1_times = t1_times[:num_qubits]  # Truncate if too many\n",
    "            \n",
    "        if isinstance(t2_times, (int, float)):\n",
    "            self.t2_times = [t2_times] * num_qubits\n",
    "        else:\n",
    "            self.t2_times = t2_times[:num_qubits]\n",
    "            \n",
    "        if isinstance(depolarizing_probs, (int, float)):\n",
    "            self.depolarizing_probs = [depolarizing_probs] * num_qubits\n",
    "        else:\n",
    "            self.depolarizing_probs = depolarizing_probs[:num_qubits]\n",
    "            \n",
    "        if isinstance(readout_errors[0], (int, float)):\n",
    "            self.readout_errors = [readout_errors] * num_qubits\n",
    "        else:\n",
    "            self.readout_errors = readout_errors[:num_qubits]\n",
    "        \n",
    "        # Default gate times if not provided\n",
    "        if gate_times is None:\n",
    "            self.gate_times = {\n",
    "                'rz': 0,       # Virtual Z-rotations are often implemented with zero time\n",
    "                'sx': 50e-9,   # 50 ns for a sqrt(X) gate\n",
    "                'x': 50e-9,    # 50 ns for an X gate\n",
    "                'cx': 300e-9,  # 300 ns for a CNOT gate\n",
    "                'reset': 1e-6  # 1 μs for reset\n",
    "            }\n",
    "        else:\n",
    "            self.gate_times = gate_times\n",
    "            \n",
    "        # Create the noise model\n",
    "        self.noise_model = self._build_noise_model()\n",
    "        \n",
    "        \n",
    "    def set_backend(self, backend: str = 'qasm_simulator') -> None:\n",
    "        \"\"\"\n",
    "        Set the backend for simulation.\n",
    "        \n",
    "        Args:\n",
    "            backend: The backend to use for simulation\n",
    "        \"\"\"\n",
    "        self.backend = backend\n",
    "    \n",
    "    def set_encoding(self, encoding_policy: str = None, encoding_map: Dict[int, List[int]] = None) -> None:\n",
    "        \"\"\"\n",
    "        Set the encoding for the circuit.\n",
    "        \n",
    "        Args:\n",
    "            encoding_policy: The policy to use for encoding the circuit\n",
    "            encoding_map: The map to use for encoding the circuit, e.g. {0: [1, 2], 1: [3]}\n",
    "        \"\"\"\n",
    "\n",
    "        self.encoding_policy = encoding_policy\n",
    "        print(\"⚠️ IMPORTANT NOTICE ⚠️\")\n",
    "        print(f\"Encoding map provided: {encoding_map}\")\n",
    "        print(\"The num_qubits parameter will be automatically updated to (max qubit index + 1)\")\n",
    "\n",
    "        self.encoding_map = encoding_map\n",
    "        if encoding_map is not None:\n",
    "            for k, v in encoding_map.items():\n",
    "                if k>=self.num_qubits:\n",
    "                    self.num_qubits = k+1\n",
    "                if max(v)>=self.num_qubits:\n",
    "                    self.num_qubits = max(v)+1\n",
    "\n",
    "    def _build_noise_model(self) -> NoiseModel:\n",
    "        \"\"\"\n",
    "        Build a noise model with the specified noise parameters.\n",
    "        \n",
    "        Returns:\n",
    "            A qiskit NoiseModel with the configured noise sources\n",
    "        \"\"\"\n",
    "        noise_model = NoiseModel()\n",
    "        \n",
    "        # Add T1/T2 thermal relaxation errors\n",
    "        for i in range(self.num_qubits):\n",
    "            # For single-qubit gates\n",
    "            for gate_name in ['x', 'sx', 'rz']:\n",
    "                if gate_name in self.gate_times:\n",
    "                    gate_time = self.gate_times[gate_name]\n",
    "                    # Create thermal relaxation error for single-qubit gate\n",
    "                    thermal_err = thermal_relaxation_error(\n",
    "                        self.t1_times[i], \n",
    "                        self.t2_times[i], \n",
    "                        gate_time\n",
    "                    )\n",
    "                    # Add error to noise model\n",
    "                    noise_model.add_quantum_error(thermal_err, gate_name, [i])\n",
    "            \n",
    "            # Add depolarizing error for single-qubit gates\n",
    "            clifford_1q = ['x', 'y', 'z', 'h', 's', 'sdg']\n",
    "            for gate_name in clifford_1q:\n",
    "                # Create depolarizing error\n",
    "                depol_err = depolarizing_error(self.depolarizing_probs[i], 1)\n",
    "                # Add error to noise model\n",
    "                noise_model.add_quantum_error(depol_err, gate_name, [i])\n",
    "        \n",
    "        # For two-qubit gates (CNOT)\n",
    "        for i in range(self.num_qubits-1):\n",
    "            for j in range(i+1, self.num_qubits):\n",
    "                # T1/T2 error for two-qubit gate\n",
    "                if 'cx' in self.gate_times:\n",
    "                    gate_time = self.gate_times['cx']\n",
    "                    # Control qubit thermal relaxation\n",
    "                    thermal_err_i = thermal_relaxation_error(\n",
    "                        self.t1_times[i], \n",
    "                        self.t2_times[i], \n",
    "                        gate_time\n",
    "                    )\n",
    "                    # Target qubit thermal relaxation\n",
    "                    thermal_err_j = thermal_relaxation_error(\n",
    "                        self.t1_times[j], \n",
    "                        self.t2_times[j], \n",
    "                        gate_time\n",
    "                    )\n",
    "                    # Combine errors\n",
    "                    thermal_err = thermal_err_i.tensor(thermal_err_j)\n",
    "                    # Add to noise model\n",
    "                    noise_model.add_quantum_error(thermal_err, 'cx', [i, j])\n",
    "                    \n",
    "                # Depolarizing error for two-qubit gate\n",
    "                # Average the error probability of both qubits\n",
    "                avg_depol_prob = (self.depolarizing_probs[i] + self.depolarizing_probs[j]) / 2\n",
    "                # Create depolarizing error\n",
    "                depol_err = depolarizing_error(avg_depol_prob, 2)\n",
    "                # Add to noise model\n",
    "                noise_model.add_quantum_error(depol_err, 'cx', [i, j])\n",
    "        \n",
    "        # Add readout errors\n",
    "        for i in range(self.num_qubits):\n",
    "            # Probabilities of flipping measurement results\n",
    "            p0given1 = self.readout_errors[i][0]  # Probability of reading 0 when qubit is in state 1\n",
    "            p1given0 = self.readout_errors[i][1]  # Probability of reading 1 when qubit is in state 0\n",
    "\n",
    "            # Create readout error\n",
    "            ro_err = ReadoutError([[1 - p0given1, p0given1], [p1given0, 1 - p1given0]])\n",
    "            print(ro_err)\n",
    "            # Add to noise model\n",
    "            noise_model.add_readout_error(ro_err, [i])\n",
    "        \n",
    "        return noise_model\n",
    "    \n",
    "    def generate_random_clifford_circuit(self, num_gates: int, encoding: bool = False, add_inverse: bool = True) -> QuantumCircuit:\n",
    "        \"\"\"\n",
    "        Generate a random Clifford circuit with a specified number of gates.\n",
    "        \n",
    "        Args:\n",
    "            num_gates: Number of random Clifford gates to include\n",
    "            encoding: Optional choice for encoding the circuit.\n",
    "            add_inverse: Optional choice to add the inverse of the circuit.\n",
    "\n",
    "        Returns:\n",
    "            A quantum circuit with random Clifford gates\n",
    "        \"\"\"\n",
    "        # Create a circuit with quantum and classical registers\n",
    "        qreg = QuantumRegister(self.num_qubits, 'q')\n",
    "        creg = ClassicalRegister(self.num_qubits, 'c')\n",
    "        circuit = QuantumCircuit(qreg, creg)\n",
    "        \n",
    "        # Define the Clifford gateset (single-qubit gates)\n",
    "        clifford_1q = ['x', 'y', 'z', 'h', 's', 'sdg']\n",
    "        \n",
    "        # Add random gates\n",
    "        if encoding == False:\n",
    "            for _ in range(num_gates):\n",
    "                # Randomly choose between 1-qubit and 2-qubit gates\n",
    "                if random.random() < 0.7 or self.num_qubits == 1:  # 70% 1-qubit gates, 30% 2-qubit gates\n",
    "                    # Single-qubit gate\n",
    "                    qubit = random.randint(0, self.num_qubits - 1)\n",
    "                    gate = random.choice(clifford_1q)\n",
    "                    \n",
    "                    # Apply the selected gate\n",
    "                    if gate == 'x':\n",
    "                        circuit.x(qreg[qubit])\n",
    "                    elif gate == 'y':\n",
    "                        circuit.y(qreg[qubit])\n",
    "                    elif gate == 'z':\n",
    "                        circuit.z(qreg[qubit])\n",
    "                    elif gate == 'h':\n",
    "                        circuit.h(qreg[qubit])\n",
    "                    elif gate == 's':\n",
    "                        circuit.s(qreg[qubit])\n",
    "                    elif gate == 'sdg':\n",
    "                        circuit.sdg(qreg[qubit])\n",
    "                else:\n",
    "                    # Two-qubit gate (CNOT)\n",
    "                    if self.num_qubits >= 2 and self.encoding_policy is None:\n",
    "                        control = random.randint(0, self.num_qubits - 1)\n",
    "                        target = random.randint(0, self.num_qubits - 1)\n",
    "                        # Make sure control and target are different\n",
    "                        while target == control:\n",
    "                            target = random.randint(0, self.num_qubits - 1)\n",
    "                        \n",
    "                        circuit.cx(qreg[control], qreg[target])\n",
    "        else:\n",
    "            if not self.encoding_map:\n",
    "                raise ValueError(\"No encoding map provided\")\n",
    "\n",
    "            op_qubits = list(self.encoding_map.keys())\n",
    "            for _ in range(num_gates):                # Randomly choose between 1-qubit and 2-qubit gates\n",
    "                if random.random() < 0.7 or len(op_qubits) == 1:  # 70% 1-qubit gates, 30% 2-qubit gates\n",
    "                    # Single-qubit gate\n",
    "                    qubit = random.choice(op_qubits)\n",
    "                    gate = random.choice(clifford_1q)\n",
    "                    \n",
    "                    # Apply the selected gate\n",
    "                    if gate == 'x':\n",
    "                        circuit.x(qreg[qubit])\n",
    "                    elif gate == 'y':\n",
    "                        circuit.y(qreg[qubit])\n",
    "                    elif gate == 'z':\n",
    "                        circuit.z(qreg[qubit])\n",
    "                    elif gate == 'h':\n",
    "                        circuit.h(qreg[qubit])\n",
    "                    elif gate == 's':\n",
    "                        circuit.s(qreg[qubit])\n",
    "                    elif gate == 'sdg':\n",
    "                        circuit.sdg(qreg[qubit])\n",
    "                else:\n",
    "                    # Two-qubit gate (CNOT)\n",
    "                    control = random.choice(op_qubits)\n",
    "                    target = random.choice(op_qubits)\n",
    "                    while target == control:\n",
    "                        target = random.choice(op_qubits)\n",
    "        \n",
    "        if add_inverse:\n",
    "            # Create a reversed circuit for the inverse operation\n",
    "            # This reverses the order of gates while preserving the target qubits\n",
    "            reversed_circuit = QuantumCircuit(circuit.qregs[0], circuit.cregs[0])\n",
    "            \n",
    "            # Iterate through the circuit data in reverse order\n",
    "            for instruction in reversed(circuit.data):\n",
    "                # Extract operation and qubits\n",
    "                operation = instruction.operation\n",
    "                qubits = instruction.qubits\n",
    "                clbits = instruction.clbits\n",
    "                \n",
    "                # Add the inverse of each gate to the reversed circuit\n",
    "                # This maintains the exact same qubits as the original operation\n",
    "                # but applies the inverse operation to them\n",
    "                reversed_circuit._append(instruction.replace(\n",
    "                    operation=operation.inverse()\n",
    "                ))\n",
    "            \n",
    "            # Compose the reversed circuit with the original\n",
    "            circuit.compose(reversed_circuit, inplace=True)\n",
    "            \n",
    "        return circuit\n",
    "\n",
    "    \n",
    "    def generate_rb_circuits(self, lengths: List[int], num_samples: int = 10, encoding: bool = False, identity_params: dict = None) -> Dict[int, List[QuantumCircuit]]:\n",
    "        \"\"\"\n",
    "        Generate circuits for randomized benchmarking with different sequence lengths.\n",
    "        \n",
    "        This function creates RB circuits by:\n",
    "        1. Generating a sequence of random Clifford gates\n",
    "        2. Computing the inverse operation to return to the initial state\n",
    "        3. Appending the inverse to ensure the circuit should ideally return to the initial state\n",
    "        \n",
    "        Args:\n",
    "            lengths: List of circuit depths to generate\n",
    "            num_samples: Number of random circuits to generate for each length\n",
    "            encoding: Optional choice for encoding the circuit.\n",
    "            identity_params: Optional choice for the number of identity gates to append to the end of the circuit.\n",
    "\n",
    "        Returns:\n",
    "            Dictionary mapping sequence lengths to lists of quantum circuits\n",
    "        \"\"\"\n",
    "        rb_circuits = {}\n",
    "        \n",
    "        for length in lengths:\n",
    "            rb_circuits[length] = []\n",
    "            for _ in range(num_samples):\n",
    "                # Create a circuit with random Clifford gates without measurements\n",
    "                circuit = self.generate_random_clifford_circuit(length, encoding=encoding, add_inverse=True)\n",
    "                qreg = circuit.qregs[0]\n",
    "                creg = circuit.cregs[0]\n",
    "\n",
    "                if encoding:\n",
    "                    if self.encoding_policy == '0011':\n",
    "                        for mp_qubit, mp_target in self.encoding_map.items():\n",
    "                            for tq in mp_target:\n",
    "                                circuit.cx(qreg[mp_qubit], qreg[tq])\n",
    "\n",
    "                    elif self.encoding_policy == '0110':\n",
    "                        for mp_qubit, mp_target in self.encoding_map.items():\n",
    "                            for tq in mp_target:\n",
    "                                circuit.cx(qreg[mp_qubit], qreg[tq])\n",
    "                                circuit.x(qreg[tq])\n",
    "                if identity_params:\n",
    "                    for _ in range(identity_params['nums']):\n",
    "                        circuit.id(qreg[identity_params['qubit']])\n",
    "                # Add measurements\n",
    "                circuit.measure(qreg, creg)\n",
    "                rb_circuits[length].append(circuit)\n",
    "                \n",
    "        return rb_circuits\n",
    "    \n",
    "    def simulate_circuits(self, circuits: Dict[int, List[QuantumCircuit]], shots: int = 1024, using_fake_backend: bool = False) -> Dict[int, List[Dict]]:\n",
    "        \"\"\"\n",
    "        Simulate a set of circuits with the configured noise model using SamplerV2.\n",
    "        \n",
    "        Args:\n",
    "            circuits: Dictionary mapping sequence lengths to lists of circuits\n",
    "            shots: Number of shots to use in the simulation\n",
    "            using_fake_backend: Optional choice to use the fake backend \n",
    "\n",
    "        Returns:\n",
    "            Dictionary mapping sequence lengths to lists of measurement results\n",
    "        \"\"\"\n",
    "        results = {}\n",
    "        \n",
    "        from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    "        from qiskit_aer import AerSimulator\n",
    "        from qiskit_ibm_runtime import SamplerV2 as Sampler\n",
    "        \n",
    "        if using_fake_backend == False:\n",
    "            # Set up the AerSimulator with noise model\n",
    "            sim_backend = AerSimulator(noise_model=self.noise_model)\n",
    "            print(self.noise_model)\n",
    "            # Create SamplerV2 with the noisy backend\n",
    "            sampler = Sampler(mode=sim_backend)\n",
    "            \n",
    "        else:\n",
    "            # Generate a pass manager for transpilation\n",
    "            from qiskit_ibm_runtime.fake_provider import FakeGuadalupeV2\n",
    "            backend = FakeGuadalupeV2()\n",
    "            # pm = generate_preset_pass_manager(1, backend)\n",
    "            # pm = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
    "\n",
    "        for length, circuit_list in circuits.items():\n",
    "            results[length] = []\n",
    "\n",
    "            # Transpile circuits for the backend\n",
    "            # transpiled_circuits = pm.run(circuit_list)    # the optimization may change the circuit and remove some noise\n",
    "            \n",
    "            if using_fake_backend == False:\n",
    "                # Execute with sampler, explicitly setting shots\n",
    "                job = sampler.run(circuit_list, shots=shots)\n",
    "                circuit_results = job.result()\n",
    "            else:\n",
    "\n",
    "                # transpile benchmark circuits  \n",
    "                # run benchmark circuits after transpile\n",
    "                job = backend.run(circuit_list, shots=shots) \n",
    "                circuit_results = job.result()\n",
    "            # Extract counts from the sampler\n",
    "            # print(results[0].data.c.get_counts())\n",
    "            for i in range(len(circuit_list)):\n",
    "                if using_fake_backend:\n",
    "                    count_dict = circuit_results.get_counts(circuit_list[i])\n",
    "                else:\n",
    "                    count_dict = circuit_results[i].data.c.get_counts()\n",
    "\n",
    "                results[length].append(count_dict)\n",
    "\n",
    "        return results\n",
    "    \n",
    "\n",
    "    def calculate_success_probabilities(self, results: Dict[int, List[Dict]], encoding: bool = False) -> Dict[int, List[float]]:\n",
    "        \"\"\"\n",
    "        Calculate success probabilities for randomized benchmarking.\n",
    "        \n",
    "        Args:\n",
    "            results: Dictionary mapping sequence lengths to lists of measurement results\n",
    "            encoding: Optional choice for encoding the circuit.\n",
    "            \n",
    "        Returns:\n",
    "            Dictionary mapping sequence lengths to lists of success probabilities\n",
    "        \"\"\"\n",
    "        success_probs = {}\n",
    "        \n",
    "        # Set default initial state if not provided\n",
    "        States = None\n",
    "        \n",
    "        initial_state = ['0' for _ in range(self.num_qubits)]\n",
    "        if encoding:\n",
    "            op_qubits = list(self.encoding_map.keys())\n",
    "            if self.encoding_policy == '0110':\n",
    "                for op_qubit in op_qubits:\n",
    "                    mp_qubits = self.encoding_map[op_qubit]\n",
    "                    for mp_qubit in mp_qubits:\n",
    "                        initial_state[mp_qubit] = '1'\n",
    "            \n",
    "        initial_state = ''.join(initial_state[::-1])\n",
    "            \n",
    "        if self.encoding_policy == '0011':\n",
    "            States = ['00', '11']\n",
    "        elif self.encoding_policy == '0110':\n",
    "            States = ['10', '01']\n",
    "\n",
    "        for length, result_list in results.items():\n",
    "            success_probs[length] = []\n",
    "            \n",
    "            for counts in result_list:\n",
    "                # Total shots\n",
    "                if States:\n",
    "                    total_shots = sum(counts[state] for state in States if state in counts)\n",
    "                else:\n",
    "                    total_shots = sum(counts.values())\n",
    "                # Count successful measurements (matching initial state)\n",
    "                successful_shots = counts.get(initial_state, 0)\n",
    "                \n",
    "                # Calculate success probability\n",
    "                # print(length, successful_shots, total_shots)\n",
    "                success_prob = successful_shots / total_shots\n",
    "                success_probs[length].append(success_prob)\n",
    "                \n",
    "        return success_probs\n",
    "    \n",
    "    def plot_results(self, success_probs: Dict[int, List[float]], title: str = None) -> plt.Figure:\n",
    "        \"\"\"\n",
    "        Plot randomized benchmarking results.\n",
    "        \n",
    "        Args:\n",
    "            success_probs: Dictionary mapping sequence lengths to lists of success probabilities\n",
    "            title: Title of the plot\n",
    "\n",
    "        Returns:\n",
    "            Matplotlib figure with the results plot\n",
    "        \"\"\"\n",
    "        # Extract data for plotting\n",
    "        lengths = sorted(success_probs.keys())\n",
    "        mean_probs = [np.mean(success_probs[length]) for length in lengths]\n",
    "        std_probs = [np.std(success_probs[length]) for length in lengths]\n",
    "        \n",
    "        # Create the plot\n",
    "        fig, ax = plt.subplots(figsize=(10, 6))\n",
    "        \n",
    "        # Plot data points with error bars\n",
    "        ax.errorbar(lengths, mean_probs, yerr=std_probs, fmt='o-', capsize=5, \n",
    "                   label='Simulation results', color='blue')\n",
    "        \n",
    "        # Fit an exponential decay curve (p = A*p^m + B)\n",
    "        # where m is the sequence length and p is the average error rate\n",
    "        try:\n",
    "            from scipy.optimize import curve_fit\n",
    "            \n",
    "            def decay_curve(m, a, p, b):\n",
    "                return a * (p ** m) + b\n",
    "            \n",
    "            # Initial guesses for parameters\n",
    "            p0 = [0.5, 0.99, 0.5]\n",
    "            \n",
    "            # Perform the fit\n",
    "            params, _ = curve_fit(decay_curve, lengths, mean_probs, p0=p0)\n",
    "            a, p, b = params\n",
    "            \n",
    "            # Generate points for the fitted curve\n",
    "            x_fit = np.linspace(min(lengths), max(lengths), 100)\n",
    "            y_fit = decay_curve(x_fit, a, p, b)\n",
    "            \n",
    "            # Plot the fitted curve\n",
    "            ax.plot(x_fit, y_fit, 'r-', label=f'Fit: $A\\\\cdot p^m + B$\\n$A = {a:.4f}$\\n$p = {p:.4f}$\\n$B = {b:.4f}$', color='red')\n",
    "            \n",
    "            # Calculate average error rate per Clifford\n",
    "            avg_error_rate = 1 - p\n",
    "            ax.text(0.6, 0.65, f'Adverage Error Rate of per Clifford gate:\\n {avg_error_rate*100/2:.2f}%', \n",
    "                   transform=ax.transAxes, fontsize=12,\n",
    "                   bbox=dict(facecolor='white', alpha=0.8))\n",
    "        except:\n",
    "            # If fitting fails, just plot the data points\n",
    "            pass\n",
    "        \n",
    "        # Set plot labels and title\n",
    "        ax.set_xlabel('Sequence Length (m)', fontsize=12)\n",
    "        ax.set_ylabel('Success Probability', fontsize=12)\n",
    "        ax.set_title(title, fontsize=14)\n",
    "        \n",
    "        # Add a grid and legend\n",
    "        ax.grid(True, linestyle='--', alpha=0.7)\n",
    "        ax.legend(fontsize=10)\n",
    "        \n",
    "        # Set axis limits\n",
    "        ax.set_ylim(0, 1.05)\n",
    "        ax.set_xlim(0, max(lengths) * 1.05)\n",
    "        \n",
    "        # Add description of noise parameters\n",
    "        noise_text = f\"Noise Parameters:\\nT1 range: {min(self.t1_times)*1e6:.1f}, {max(self.t1_times)*1e6:.1f} µs\\n\"\n",
    "        noise_text += f\"T2 range: {min(self.t2_times)*1e6:.1f}, {max(self.t2_times)*1e6:.1f} µs\\n\"\n",
    "        noise_text += f\"Depol prob: {min(self.depolarizing_probs):.5f}, {max(self.depolarizing_probs):.5f}\\n\"\n",
    "        noise_text += f\"Readout err: {min([e[0] for e in self.readout_errors]):.3f}, {max([e[0] for e in self.readout_errors]):.3f}\"\n",
    "        \n",
    "        ax.text(0.8, 0.02, noise_text, transform=ax.transAxes, fontsize=8,\n",
    "               bbox=dict(facecolor='white', alpha=0.8))\n",
    "        \n",
    "        # Improve layout and return the figure\n",
    "        fig.tight_layout()\n",
    "        return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example_benchmark.py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os\n",
    "\n",
    "SAVE_PATH = 'plots'\n",
    "if not os.path.exists(SAVE_PATH):\n",
    "    os.makedirs(SAVE_PATH)\n",
    "shots = int(1e3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: quantum error already exists for instruction \"x\" on qubits (0,) , appending additional error.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ReadoutError on 1 qubits. Assignment probabilities:\n",
      " P(j|0) =  [0.9955 0.0045]\n",
      " P(j|1) =  [0.019 0.981]\n",
      "⚠️ IMPORTANT NOTICE ⚠️\n",
      "Encoding map provided: None\n",
      "The num_qubits parameter will be automatically updated to (max qubit index + 1)\n",
      "Generating random benchmarking circuits...\n",
      "Simulating circuits with noise model...\n",
      "Calculating success probabilities...\n",
      "Plotting results...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\13742\\AppData\\Local\\Temp\\ipykernel_69744\\3914844999.py:499: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"r-\" (-> color='r'). The keyword argument will take precedence.\n",
      "  ax.plot(x_fit, y_fit, 'r-', label=f'Fit: $A\\\\cdot p^m + B$\\n$A = {a:.4f}$\\n$p = {p:.4f}$\\n$B = {b:.4f}$', color='red')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results saved to 'random_benchmarking_results.png'\n",
      "Average success probabilities for each sequence length:\n",
      "  Length 1: 0.9933\n",
      "  Length 10: 0.9938\n",
      "  Length 25: 0.9921\n",
      "  Length 50: 0.9892\n",
      "  Length 100: 0.9817\n",
      "  Length 250: 0.9630\n",
      "  Length 500: 0.9410\n",
      "  Length 1000: 0.8919\n",
      "  Length 2000: 0.8124\n",
      "  Length 3500: 0.7197\n",
      "  Length 5000: 0.6583\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a benchmark instance with custom noise parameters\n",
    "benchmark = NoisyBenchmark(\n",
    "    num_qubits=1,   # actual qubits\n",
    "    t1_times=[25.2821e-6, 8.7069e-6],  # T1 times in seconds (microseconds)\n",
    "    t2_times=[3.3763e-6, 4.0429e-6],  # T2 times in seconds (microseconds)\n",
    "    depolarizing_probs=[0.0026, 0.0026],  # Depolarizing error probabilities\n",
    "    readout_errors=[(0.0045, 0.019), (0.002, 0.0255)]  # Readout error probabilities\n",
    ")\n",
    "\n",
    "encoding_policy = 'No encoding'\n",
    "encoding_map = None\n",
    "encoding = False\n",
    "benchmark.set_encoding(encoding_policy=encoding_policy, encoding_map=encoding_map)\n",
    "# Generate circuits for randomized benchmarking\n",
    "# Define sequence lengths for randomized benchmarking\n",
    "# Use exponentially increasing sequence lengths to efficiently capture\n",
    "# the decay curve from short sequences to very long ones\n",
    "sequence_lengths = [1, 10, 25, 50, 100, 250, 500, 1000, 2000, 3500, 5000]\n",
    "# Use sufficient samples for statistical significance while balancing computation time\n",
    "num_samples = 10  # Number of random circuit variants per sequence length\n",
    "\n",
    "print(\"Generating random benchmarking circuits...\")\n",
    "rb_circuits = benchmark.generate_rb_circuits(sequence_lengths, num_samples, encoding=encoding)\n",
    "\n",
    "print(\"Simulating circuits with noise model...\")\n",
    "rb_results = benchmark.simulate_circuits(rb_circuits, shots=shots, using_fake_backend=True)\n",
    "\n",
    "print(\"Calculating success probabilities...\")\n",
    "success_probs_no_encoding = benchmark.calculate_success_probabilities(rb_results, encoding=encoding)\n",
    "\n",
    "print(\"Plotting results...\")\n",
    "title = f'Randomized Benchmarking Results ({benchmark.encoding_policy} encoding)'\n",
    "fig = benchmark.plot_results(success_probs_no_encoding, title=title)\n",
    "fig.savefig(os.path.join(SAVE_PATH, f'{title}.png'), dpi=1200)\n",
    "\n",
    "print(f\"Results saved to 'random_benchmarking_results.png'\")\n",
    "print(\"Average success probabilities for each sequence length:\")\n",
    "mean_probs_no_encoding = np.zeros(len(sequence_lengths))\n",
    "for i, length in enumerate(sequence_lengths):\n",
    "    mean_prob = np.mean(success_probs_no_encoding[length])\n",
    "    mean_probs_no_encoding[i] = mean_prob\n",
    "    print(f\"  Length {length}: {mean_prob:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: quantum error already exists for instruction \"x\" on qubits (0,) , appending additional error.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ReadoutError on 1 qubits. Assignment probabilities:\n",
      " P(j|0) =  [0.9955 0.0045]\n",
      " P(j|1) =  [0.019 0.981]\n",
      "⚠️ IMPORTANT NOTICE ⚠️\n",
      "Encoding map provided: None\n",
      "The num_qubits parameter will be automatically updated to (max qubit index + 1)\n",
      "Generating random benchmarking circuits...\n",
      "Simulating circuits with noise model...\n",
      "Calculating success probabilities...\n",
      "Plotting results...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\13742\\AppData\\Local\\Temp\\ipykernel_69744\\3914844999.py:499: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"r-\" (-> color='r'). The keyword argument will take precedence.\n",
      "  ax.plot(x_fit, y_fit, 'r-', label=f'Fit: $A\\\\cdot p^m + B$\\n$A = {a:.4f}$\\n$p = {p:.4f}$\\n$B = {b:.4f}$', color='red')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results saved to 'random_benchmarking_results.png'\n",
      "Average success probabilities for each sequence length:\n",
      "  Length 1: 0.9940\n",
      "  Length 10: 0.9921\n",
      "  Length 25: 0.9902\n",
      "  Length 50: 0.9871\n",
      "  Length 100: 0.9818\n",
      "  Length 250: 0.9681\n",
      "  Length 500: 0.9353\n",
      "  Length 1000: 0.8918\n",
      "  Length 2000: 0.8102\n",
      "  Length 3500: 0.7136\n",
      "  Length 5000: 0.6403\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a benchmark instance with custom noise parameters\n",
    "benchmark = NoisyBenchmark(\n",
    "    num_qubits=1,   # actual qubits\n",
    "    t1_times=[25.2821e-6, 8.7069e-6],  # T1 times in seconds (microseconds)\n",
    "    t2_times=[3.3763e-6, 4.0429e-6],  # T2 times in seconds (microseconds)\n",
    "    depolarizing_probs=[0.0026, 0.0026],  # Depolarizing error probabilities\n",
    "    readout_errors=[(0.0045, 0.019), (0.002, 0.0255)]  # Readout error probabilities\n",
    ")\n",
    "\n",
    "encoding_policy = '0011-rf'\n",
    "encoding_map = None\n",
    "encoding = False\n",
    "benchmark.set_encoding(encoding_policy=encoding_policy, encoding_map=encoding_map)\n",
    "# Generate circuits for randomized benchmarking\n",
    "# sequence_lengths = [1, 10, 20, 50, 100, 200, 300, 500]\n",
    "# num_samples = 10  # Number of circuits per sequence length\n",
    "\n",
    "print(\"Generating random benchmarking circuits...\")\n",
    "rb_circuits = benchmark.generate_rb_circuits(sequence_lengths, num_samples, encoding=encoding)\n",
    "\n",
    "print(\"Simulating circuits with noise model...\")\n",
    "rb_results = benchmark.simulate_circuits(rb_circuits, shots=shots, using_fake_backend=True)\n",
    "\n",
    "print(\"Calculating success probabilities...\")\n",
    "success_probs_0110rf = benchmark.calculate_success_probabilities(rb_results, encoding=encoding)\n",
    "\n",
    "print(\"Plotting results...\")\n",
    "title = f'Randomized Benchmarking Results ({benchmark.encoding_policy} encoding)'\n",
    "fig = benchmark.plot_results(success_probs_0110rf, title=title)\n",
    "fig.savefig(os.path.join(SAVE_PATH, f'{title}.png'), dpi=1200)\n",
    "\n",
    "print(f\"Results saved to 'random_benchmarking_results.png'\")\n",
    "print(\"Average success probabilities for each sequence length:\")\n",
    "mean_probs_0110rf = np.zeros(len(sequence_lengths))\n",
    "for i, length in enumerate(sequence_lengths):\n",
    "    mean_probs_0110rf[i] = np.mean(success_probs_0110rf[length])\n",
    "    print(f\"  Length {length}: {mean_probs_0110rf[i]:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: quantum error already exists for instruction \"x\" on qubits (0,) , appending additional error.\n",
      "WARNING: quantum error already exists for instruction \"x\" on qubits (1,) , appending additional error.\n",
      "WARNING: quantum error already exists for instruction \"cx\" on qubits (0, 1) , appending additional error.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ReadoutError on 1 qubits. Assignment probabilities:\n",
      " P(j|0) =  [0.9955 0.0045]\n",
      " P(j|1) =  [0.019 0.981]\n",
      "ReadoutError on 1 qubits. Assignment probabilities:\n",
      " P(j|0) =  [0.998 0.002]\n",
      " P(j|1) =  [0.0255 0.9745]\n",
      "⚠️ IMPORTANT NOTICE ⚠️\n",
      "Encoding map provided: {0: [1]}\n",
      "The num_qubits parameter will be automatically updated to (max qubit index + 1)\n",
      "Generating random benchmarking circuits...\n",
      "Simulating circuits with noise model...\n",
      "Calculating success probabilities...\n",
      "Plotting results...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\13742\\AppData\\Local\\Temp\\ipykernel_69744\\3914844999.py:499: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"r-\" (-> color='r'). The keyword argument will take precedence.\n",
      "  ax.plot(x_fit, y_fit, 'r-', label=f'Fit: $A\\\\cdot p^m + B$\\n$A = {a:.4f}$\\n$p = {p:.4f}$\\n$B = {b:.4f}$', color='red')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results saved to 'random_benchmarking_results.png'\n",
      "Average success probabilities for each sequence length:\n",
      "  Length 1: 0.9989\n",
      "  Length 10: 0.9978\n",
      "  Length 25: 0.9965\n",
      "  Length 50: 0.9943\n",
      "  Length 100: 0.9878\n",
      "  Length 250: 0.9690\n",
      "  Length 500: 0.9486\n",
      "  Length 1000: 0.9009\n",
      "  Length 2000: 0.8185\n",
      "  Length 3500: 0.7307\n",
      "  Length 5000: 0.6629\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a benchmark instance with custom noise parameters\n",
    "benchmark = NoisyBenchmark(\n",
    "    num_qubits=2,   # actual qubits\n",
    "    t1_times=[25.2821e-6, 8.7069e-6],  # T1 times in seconds (microseconds)\n",
    "    t2_times=[3.3763e-6, 4.0429e-6],  # T2 times in seconds (microseconds)\n",
    "    depolarizing_probs=[0.0026, 0.0026],  # Depolarizing error probabilities\n",
    "    readout_errors=[(0.0045, 0.019), (0.002, 0.0255)]  # Readout error probabilities\n",
    ")\n",
    "\n",
    "\n",
    "encoding_policy = '0011'\n",
    "encoding_map = {0: [1]}\n",
    "encoding = True\n",
    "benchmark.set_encoding(encoding_policy=encoding_policy, encoding_map=encoding_map)\n",
    "# Generate circuits for randomized benchmarking\n",
    "# sequence_lengths = [1, 10, 20, 50, 100, 200, 300, 500]\n",
    "# num_samples = 10  # Number of circuits per sequence length\n",
    "\n",
    "print(\"Generating random benchmarking circuits...\")\n",
    "rb_circuits = benchmark.generate_rb_circuits(sequence_lengths, num_samples, encoding=encoding)\n",
    "\n",
    "print(\"Simulating circuits with noise model...\")\n",
    "rb_results = benchmark.simulate_circuits(rb_circuits, shots=shots, using_fake_backend=True)\n",
    "\n",
    "print(\"Calculating success probabilities...\")\n",
    "success_probs_0011 = benchmark.calculate_success_probabilities(rb_results)\n",
    "\n",
    "print(\"Plotting results...\")\n",
    "title = f'Randomized Benchmarking Results ({benchmark.encoding_policy} encoding)'\n",
    "fig = benchmark.plot_results(success_probs_0011, title=title)\n",
    "fig.savefig(os.path.join(SAVE_PATH, f'{title}.png'), dpi=1200)\n",
    "\n",
    "print(f\"Results saved to 'random_benchmarking_results.png'\")\n",
    "print(\"Average success probabilities for each sequence length:\")\n",
    "mean_probs_0011 = np.zeros(len(sequence_lengths))\n",
    "for i, length in enumerate(sequence_lengths):\n",
    "    mean_probs_0011[i] = np.mean(success_probs_0011[length])\n",
    "    print(f\"  Length {length}: {mean_probs_0011[i]:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: quantum error already exists for instruction \"x\" on qubits (0,) , appending additional error.\n",
      "WARNING: quantum error already exists for instruction \"x\" on qubits (1,) , appending additional error.\n",
      "WARNING: quantum error already exists for instruction \"cx\" on qubits (0, 1) , appending additional error.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ReadoutError on 1 qubits. Assignment probabilities:\n",
      " P(j|0) =  [0.9955 0.0045]\n",
      " P(j|1) =  [0.019 0.981]\n",
      "ReadoutError on 1 qubits. Assignment probabilities:\n",
      " P(j|0) =  [0.998 0.002]\n",
      " P(j|1) =  [0.0255 0.9745]\n",
      "⚠️ IMPORTANT NOTICE ⚠️\n",
      "Encoding map provided: {0: [1]}\n",
      "The num_qubits parameter will be automatically updated to (max qubit index + 1)\n",
      "Generating random benchmarking circuits...\n",
      "Simulating circuits with noise model...\n",
      "Calculating success probabilities...\n",
      "Plotting results...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\13742\\AppData\\Local\\Temp\\ipykernel_69744\\3914844999.py:499: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"r-\" (-> color='r'). The keyword argument will take precedence.\n",
      "  ax.plot(x_fit, y_fit, 'r-', label=f'Fit: $A\\\\cdot p^m + B$\\n$A = {a:.4f}$\\n$p = {p:.4f}$\\n$B = {b:.4f}$', color='red')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results saved to 'random_benchmarking_results.png'\n",
      "Average success probabilities for each sequence length:\n",
      "  Length 1: 0.9984\n",
      "  Length 10: 0.9976\n",
      "  Length 25: 0.9949\n",
      "  Length 50: 0.9932\n",
      "  Length 100: 0.9874\n",
      "  Length 250: 0.9688\n",
      "  Length 500: 0.9402\n",
      "  Length 1000: 0.8884\n",
      "  Length 2000: 0.8055\n",
      "  Length 3500: 0.7114\n",
      "  Length 5000: 0.6353\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a benchmark instance with custom noise parameters\n",
    "benchmark = NoisyBenchmark(\n",
    "    num_qubits=2,   # actual qubits\n",
    "    t1_times=[25.2821e-6, 8.7069e-6],  # T1 times in seconds (microseconds)\n",
    "    t2_times=[3.3763e-6, 4.0429e-6],  # T2 times in seconds (microseconds)\n",
    "    depolarizing_probs=[0.0026, 0.0026],  # Depolarizing error probabilities\n",
    "    readout_errors=[(0.0045, 0.019), (0.002, 0.0255)]  # Readout error probabilities\n",
    ")\n",
    "\n",
    "\n",
    "encoding_policy = '0110'\n",
    "encoding_map = {0: [1]}\n",
    "encoding = True\n",
    "benchmark.set_encoding(encoding_policy=encoding_policy, encoding_map=encoding_map)\n",
    "# Generate circuits for randomized benchmarking\n",
    "# sequence_lengths = [1, 10, 20, 50, 100, 200, 300, 500]\n",
    "# num_samples = 10  # Number of circuits per sequence length\n",
    "\n",
    "\n",
    "print(\"Generating random benchmarking circuits...\")\n",
    "rb_circuits = benchmark.generate_rb_circuits(sequence_lengths, num_samples, encoding=encoding, identity_params={'nums': 50, 'qubit': 1})\n",
    "\n",
    "print(\"Simulating circuits with noise model...\")\n",
    "rb_results = benchmark.simulate_circuits(rb_circuits, shots=shots, using_fake_backend=True)\n",
    "\n",
    "print(\"Calculating success probabilities...\")\n",
    "success_probs_0110 = benchmark.calculate_success_probabilities(rb_results, encoding=encoding)\n",
    "\n",
    "print(\"Plotting results...\")\n",
    "title = f'Randomized Benchmarking Results ({benchmark.encoding_policy} encoding)'\n",
    "fig = benchmark.plot_results(success_probs_0110, title=title)\n",
    "fig.savefig(os.path.join(SAVE_PATH, f'{title}.png'), dpi=1200)\n",
    "\n",
    "print(f\"Results saved to 'random_benchmarking_results.png'\")\n",
    "print(\"Average success probabilities for each sequence length:\")\n",
    "mean_probs_0110 = np.zeros(len(sequence_lengths))\n",
    "for i, length in enumerate(sequence_lengths):\n",
    "    mean_probs_0110[i] = np.mean(success_probs_0110[length])\n",
    "    print(f\"  Length {length}: {mean_probs_0110[i]:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = sequence_lengths\n",
    "plt.plot(x, mean_probs_no_encoding, label='No encoding')\n",
    "plt.plot(x, mean_probs_0011, label='0011 encoding')\n",
    "plt.plot(x, mean_probs_0110rf, label='0011-rf encoding')\n",
    "plt.plot(x, mean_probs_0110, label='0110 encoding')\n",
    "plt.xlabel('Sequence Length')\n",
    "plt.ylabel('Success Probability')\n",
    "plt.title('Randomized Benchmarking Results')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.21"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
